op {
  graph_op_name: "ScatterNd"
  in_arg {
    name: "indices"
    description: <<END
Index tensor.
END
  }
  in_arg {
    name: "updates"
    description: <<END
Updates to scatter into output.
END
  }
  in_arg {
    name: "shape"
    description: <<END
1-D. The shape of the resulting tensor.
END
  }
  out_arg {
    name: "output"
    description: <<END
A new tensor with the given shape and updates applied according
to the indices.
END
  }
  summary: "Scatter `updates` into a new tensor according to `indices`."
  description: <<END
Creates a new tensor by applying sparse `updates` to individual values or
slices within a tensor (initially zero for numeric, empty for string) of
the given `shape` according to indices.  This operator is the inverse of the
`tf.gather_nd` operator which extracts values or slices from a given tensor.

This operation is similar to tensor_scatter_add, except that the tensor is
zero-initialized. Calling `tf.scatter_nd(indices, values, shape)` is identical
to `tensor_scatter_add(tf.zeros(shape, values.dtype), indices, values)`

If `indices` contains duplicates, then their updates are accumulated (summed).

**WARNING**: The order in which updates are applied is nondeterministic, so the
output will be nondeterministic if `indices` contains duplicates -- because
of some numerical approximation issues, numbers summed in different order
may yield different results.

`indices` is an integer tensor containing indices into a new tensor of shape
`shape`.  The last dimension of `indices` can be at most the rank of `shape`:

    indices.shape[-1] <= shape.rank

The last dimension of `indices` corresponds to indices into elements
(if `indices.shape[-1] = shape.rank`) or slices
(if `indices.shape[-1] < shape.rank`) along dimension `indices.shape[-1]` of
`shape`.  `updates` is a tensor with shape

    indices.shape[:-1] + shape[indices.shape[-1]:]

The simplest form of scatter is to insert individual elements in a tensor by
index. For example, say we want to insert 4 scattered elements in a rank-1
tensor with 8 elements.

<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
<img style="width:100%" src="https://www.tensorflow.org/images/ScatterNd1.png" alt>
</div>

In Python, this scatter operation would look like this:

```python
    indices = tf.constant([[4], [3], [1], [7]])
    updates = tf.constant([9, 10, 11, 12])
    shape = tf.constant([8])
    scatter = tf.scatter_nd(indices, updates, shape)
    with tf.Session() as sess:
      print(sess.run(scatter))
```

The resulting tensor would look like this:

    [0, 11, 0, 10, 9, 0, 0, 12]

We can also, insert entire slices of a higher rank tensor all at once. For
example, if we wanted to insert two slices in the first dimension of a
rank-3 tensor with two matrices of new values.

<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
<img style="width:100%" src="https://www.tensorflow.org/images/ScatterNd2.png" alt>
</div>

In Python, this scatter operation would look like this:

```python
    indices = tf.constant([[0], [2]])
    updates = tf.constant([[[5, 5, 5, 5], [6, 6, 6, 6],
                            [7, 7, 7, 7], [8, 8, 8, 8]],
                           [[5, 5, 5, 5], [6, 6, 6, 6],
                            [7, 7, 7, 7], [8, 8, 8, 8]]])
    shape = tf.constant([4, 4, 4])
    scatter = tf.scatter_nd(indices, updates, shape)
    with tf.Session() as sess:
      print(sess.run(scatter))
```

The resulting tensor would look like this:

    [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
     [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
     [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
     [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]]

Note that on CPU, if an out of bound index is found, an error is returned.
On GPU, if an out of bound index is found, the index is ignored.
END
}
